Abstract
Bacterial Foraging Optimization (BFO) is a novel optimization algorithm based on the social foraging behavior of E. coli bacteria. However, the original BFO algorithm possesses a poor convergence behavior compared to the other successful nature-inspired algorithms. This paper presents a variation on the original BFO algorithm, namely the Cooperative Bacterial Foraging Optimization (CBFO). The cooperative approaches used here resulted in a significant improvement in the performance of the original BFO algorithm in terms of convergence speed, accuracy and robustness. The experiments compare the performance of two variants of CBFO with the original BFO, the standard PSO and a real-coded GA on a set of 4 widely-used benchmark functions. The proposed new method shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.
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